The present invention relates generally to computer vision and pattern recognition, and in particular to video analysis for detecting the presence of smoke or flame as indicative of a fire.
The ability to detect the presence of flame or smoke is important on a number of levels, including with respect to human safety and the safety of property. In particular, because of the rapid expansion rate of a fire, it is important to detect the presence of a fire as early as possible. Traditional means of detecting fire include particle sampling (i.e., smoke detectors) and temperature sensors. While accurate, these methods include a number of drawbacks. For instance, traditional particle or smoke detectors require smoke to physically reach a sensor. In some applications, the location of the fire or the presence of ventilated air systems prevents smoke from reaching the detector for an extended length of time, allowing the fire time to spread. A typical temperature sensor requires the sensor to be located physically close to the fire, because the temperature sensor will not sense a fire until it has spread to the location of the temperature sensor. In addition, neither of these systems provides as much data as might be desired regarding size, location, or intensity of the fire.
Video detection of a fire provides solutions to some of these problems. A number of video content analysis algorithms for detecting fire are known in the prior art. However, the typical video content analysis algorithms known in prior art are not effective at quickly recognizing smoke or fire. For instance, some video content analysis algorithms are only capable of either detecting flame or smoke, but not both. In other video content analysis algorithms, the presence of fire or smoke is incorrectly detected, resulting in false alarms.
Therefore, it would be beneficial to develop an improved method of analyzing video data to detect the presence of smoke and flame.